{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wTL6mJy5TQw5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vr39IczaqAv2"
   },
   "outputs": [],
   "source": [
    "train_samples = []\n",
    "train_labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fkUqUq_XqJjq"
   },
   "outputs": [],
   "source": [
    "for i in range(50):\n",
    "    random_younger = randint(13, 64)\n",
    "    train_samples.append(random_younger)\n",
    "    train_labels.append(1) \n",
    "\n",
    "    random_older = randint(65, 100)\n",
    "    train_samples.append(random_older)\n",
    "    train_labels.append(0)\n",
    "\n",
    "for i in range(1000):\n",
    "    random_younger = randint(13, 64)\n",
    "    train_samples.append(random_younger)\n",
    "    train_labels.append(0)\n",
    "\n",
    "    random_older = randint(65, 100)\n",
    "    train_samples.append(random_older)\n",
    "    train_labels.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Du2Vi0yrpkb"
   },
   "outputs": [],
   "source": [
    "train_labels_np = np.array(train_labels)\n",
    "train_samples_np = np.array(train_samples)\n",
    "train_labels_np, train_samples_np = shuffle(train_labels_np, train_samples_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 530,
     "status": "ok",
     "timestamp": 1591015544526,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "MiOd0fXxt6f7",
    "outputId": "7f9818e1-99b3-4927-eef6-39013d5b5ba6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 ... 1 0 1]\n",
      "[82 69 95 ... 81 56 77]\n",
      "[[82]\n",
      " [69]\n",
      " [95]\n",
      " ...\n",
      " [81]\n",
      " [56]\n",
      " [77]]\n",
      "2100\n",
      "2100\n"
     ]
    }
   ],
   "source": [
    "print(train_labels_np)\n",
    "print(train_samples_np)\n",
    "print(train_samples_np.reshape(-1,1))\n",
    "print(len(train_labels_np))\n",
    "print(len(train_samples_np))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5sh0N80_t80Z"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_train_samples = scaler.fit_transform(train_samples_np.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 515,
     "status": "ok",
     "timestamp": 1591015548536,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "4CDZRSDCu8TY",
    "outputId": "9b46cc03-deab-4d71-a81d-12716fada734"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.79310345]\n",
      " [0.64367816]\n",
      " [0.94252874]\n",
      " ...\n",
      " [0.7816092 ]\n",
      " [0.49425287]\n",
      " [0.73563218]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled_train_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tmk7CjXQvB05"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 821,
     "status": "ok",
     "timestamp": 1591015553966,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "txl3NrxvvU5o",
    "outputId": "14e77e34-59a4-4abb-83e5-8d4282393bb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 16)                32        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 66        \n",
      "=================================================================\n",
      "Total params: 642\n",
      "Trainable params: 642\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Dense(units=16, input_shape=(1,), activation=\"relu\"),\n",
    "    Dense(units=32, activation=\"relu\"),\n",
    "    Dense(units=2, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HL_hAfKfvfW2"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 731
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5955,
     "status": "ok",
     "timestamp": 1591015564723,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "pK_FnVfBwiSm",
    "outputId": "4704d27c-3088-4d18-8317-c0b55e2f53be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1680 samples, validate on 420 samples\n",
      "Epoch 1/20\n",
      " - 0s - loss: 0.7123 - accuracy: 0.4310 - val_loss: 0.6939 - val_accuracy: 0.5143\n",
      "Epoch 2/20\n",
      " - 0s - loss: 0.6796 - accuracy: 0.5226 - val_loss: 0.6632 - val_accuracy: 0.5786\n",
      "Epoch 3/20\n",
      " - 0s - loss: 0.6496 - accuracy: 0.6357 - val_loss: 0.6302 - val_accuracy: 0.6810\n",
      "Epoch 4/20\n",
      " - 0s - loss: 0.6178 - accuracy: 0.7173 - val_loss: 0.6012 - val_accuracy: 0.7095\n",
      "Epoch 5/20\n",
      " - 0s - loss: 0.5902 - accuracy: 0.7470 - val_loss: 0.5743 - val_accuracy: 0.7429\n",
      "Epoch 6/20\n",
      " - 0s - loss: 0.5630 - accuracy: 0.7762 - val_loss: 0.5475 - val_accuracy: 0.7762\n",
      "Epoch 7/20\n",
      " - 0s - loss: 0.5360 - accuracy: 0.7905 - val_loss: 0.5211 - val_accuracy: 0.8000\n",
      "Epoch 8/20\n",
      " - 0s - loss: 0.5097 - accuracy: 0.8137 - val_loss: 0.4958 - val_accuracy: 0.8190\n",
      "Epoch 9/20\n",
      " - 0s - loss: 0.4840 - accuracy: 0.8369 - val_loss: 0.4712 - val_accuracy: 0.8357\n",
      "Epoch 10/20\n",
      " - 0s - loss: 0.4594 - accuracy: 0.8446 - val_loss: 0.4474 - val_accuracy: 0.8643\n",
      "Epoch 11/20\n",
      " - 0s - loss: 0.4357 - accuracy: 0.8565 - val_loss: 0.4248 - val_accuracy: 0.8690\n",
      "Epoch 12/20\n",
      " - 0s - loss: 0.4141 - accuracy: 0.8655 - val_loss: 0.4046 - val_accuracy: 0.8762\n",
      "Epoch 13/20\n",
      " - 0s - loss: 0.3948 - accuracy: 0.8774 - val_loss: 0.3862 - val_accuracy: 0.8857\n",
      "Epoch 14/20\n",
      " - 0s - loss: 0.3774 - accuracy: 0.8911 - val_loss: 0.3702 - val_accuracy: 0.8857\n",
      "Epoch 15/20\n",
      " - 0s - loss: 0.3625 - accuracy: 0.8905 - val_loss: 0.3560 - val_accuracy: 0.8952\n",
      "Epoch 16/20\n",
      " - 0s - loss: 0.3494 - accuracy: 0.8970 - val_loss: 0.3436 - val_accuracy: 0.9024\n",
      "Epoch 17/20\n",
      " - 0s - loss: 0.3382 - accuracy: 0.9024 - val_loss: 0.3328 - val_accuracy: 0.9024\n",
      "Epoch 18/20\n",
      " - 0s - loss: 0.3286 - accuracy: 0.9077 - val_loss: 0.3237 - val_accuracy: 0.9024\n",
      "Epoch 19/20\n",
      " - 0s - loss: 0.3205 - accuracy: 0.9071 - val_loss: 0.3160 - val_accuracy: 0.9190\n",
      "Epoch 20/20\n",
      " - 0s - loss: 0.3136 - accuracy: 0.9173 - val_loss: 0.3091 - val_accuracy: 0.9190\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x149768d10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=scaled_train_samples, y=train_labels_np, validation_split=0.2, batch_size=10,  epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 904,
     "status": "ok",
     "timestamp": 1591015575586,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "eKfbGRc73zlN",
    "outputId": "ebde0e39-2a54-4d67-d92a-8a6f5df92a33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 34 100  35  83  51  67  57  87  49  76  18  68  61  99  50  72  32 100\n",
      "  29  82  59  90  60  93  49  78  52  86  33  76  51  77  17  88  36  65\n",
      "  40  83  33  84  18  79  53  90  30  92  17  84  29  99  13  79  13  87\n",
      "  55  93  61  73  15  81  50  87  40 100  54  89  50  70  61  72  36  69\n",
      "  29  92  46  82  34 100  53  76  40  92  51  77  19  73  21  98  29  65\n",
      "  48  75  16  92  44  71  50  69  14  81  34  96  60  85  40  72  29  82\n",
      "  40  98  25  71  36  89  60  80  22 100  57  92  32  72  35  94  49  66\n",
      "  61  90  26  94  41  92  16  91  41  78  45  83  16  84  19  95  47  96\n",
      "  29  70  42  80  14  78  58  73  21 100  59  85  55  96  39  83  53  86\n",
      "  18  67  25  98  46  74  26  99  23  88  27  75  46  69  31  65  49  69\n",
      "  41  82  51  69  54  77  31  69  27  87  27  66  54  95  34  75  37  70\n",
      "  58  96  51  92  23  94  43  67  27  65  51  89  43  69  25  73  22  86\n",
      "  62  80  23  81  46  72  58  73  57  98  36  82  54  91  37  76  54  67\n",
      "  19  92  54  91  14  89  60  81  25  74  55  80  36  91  38  96  64  67\n",
      "  57  99  44  70  60  78  23  73  24 100  53  75  15  72  58  92  17  86\n",
      "  54  67  62  94  59  99  15  83  35  99  56  85  30 100  15  69  41  75\n",
      "  57  73  40  70  57  79  50  97  53  92  17  93  26  69  25  87  22  74\n",
      "  47  82  54  86  42  97  31  92  56  73  60  84  45  69  50  85  34  84\n",
      "  26  78  35  83  58  83  21  76  45  69  36  89  21  95  63  83  44  83\n",
      "  42  76  30  87  28  73  31  84  43  95  22  78  16  99  16  91  28  91\n",
      "  45 100  56  79  37  80  43  68  62  79  52  95  19  86  25  72  33  99\n",
      "  30  66  58  65  29  85  42  65  41  82  57  88  46 100  25  74  27  98\n",
      "  61 100  61  90  51  78  58  68  44  97  36  68  51  77  32  83  45  78\n",
      "  53  73  50  96  38  80]\n"
     ]
    }
   ],
   "source": [
    "test_labels = []\n",
    "test_samples = []\n",
    "\n",
    "for i in range(10):\n",
    "    random_younger = randint(13, 64)\n",
    "    test_samples.append(random_younger)\n",
    "    test_labels.append(1)\n",
    "\n",
    "    random_older = randint(65,100)\n",
    "    test_samples.append(random_older)\n",
    "    test_labels.append(0)\n",
    "\n",
    "for i in range(200):\n",
    "    random_younger = randint(13,64)\n",
    "    test_samples.append(random_younger)\n",
    "    test_labels.append(0)\n",
    "\n",
    "    random_older = randint(65, 100)\n",
    "    test_samples.append(random_older)\n",
    "    test_labels.append(1)\n",
    "\n",
    "test_labels = np.array(test_labels)\n",
    "test_samples = np.array(test_samples)\n",
    "scaled_test_samples = scaler.fit_transform(test_samples.reshape(-1,1))\n",
    "print(test_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 798,
     "status": "ok",
     "timestamp": 1591015580044,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "XBc2-IqRbiAa",
    "outputId": "511bc2a4-3538-4481-9d3f-014ee2154c00"
   },
   "outputs": [
    {
     "data": {
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       "       [0.77011494]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_test_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J2RXgCOyb87H"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(x=scaled_test_samples, batch_size=10,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5166,
     "status": "ok",
     "timestamp": 1590893773004,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "O7BNmZ31cLiu",
    "outputId": "bce7746e-b2b0-4a9c-c7fd-4bc5ff06211c"
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
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      "[0.68900144 0.31099856]\n",
      "[0.3285391 0.6714609]\n",
      "[0.6326834 0.3673166]\n",
      "[0.20004316 0.7999568 ]\n",
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      "[0.3285391 0.6714609]\n",
      "[0.9116633  0.08833666]\n",
      "[0.09752298 0.902477  ]\n",
      "[0.9116633  0.08833666]\n",
      "[0.38625285 0.6137471 ]\n",
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      "[0.10516099 0.894839  ]\n",
      "[0.46817344 0.53182656]\n",
      "[0.16277368 0.8372264 ]\n",
      "[0.9103223  0.08967771]\n",
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      "[0.2755769 0.7244231]\n",
      "[0.55184644 0.4481535 ]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.5724947  0.42750528]\n",
      "[0.04672239 0.9532775 ]\n",
      "[0.8813092  0.11869076]\n",
      "[0.14117804 0.858822  ]\n",
      "[0.6326834 0.3673166]\n",
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      "[0.8745715  0.12542857]\n",
      "[0.21380827 0.78619176]\n",
      "[0.6326834 0.3673166]\n",
      "[0.36656216 0.6334378 ]\n",
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      "[0.06658153 0.9334185 ]\n",
      "[0.6326834 0.3673166]\n",
      "[0.07179504 0.928205  ]\n",
      "[0.906726   0.09327398]\n",
      "[0.08371867 0.91628134]\n",
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      "[0.15166047 0.84833944]\n",
      "[0.91110426 0.08889569]\n",
      "[0.24336565 0.7566343 ]\n",
      "[0.6129769 0.3870231]\n",
      "[0.16277368 0.8372264 ]\n",
      "[0.8813092  0.11869076]\n",
      "[0.07179504 0.928205  ]\n",
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      "[0.42671058 0.5732894 ]\n",
      "[0.36656216 0.6334378 ]\n",
      "[0.5724947  0.42750528]\n",
      "[0.04435072 0.95564926]\n",
      "[0.79943657 0.20056346]\n",
      "[0.310303   0.68969697]\n",
      "[0.5100806  0.48991945]\n",
      "[0.18695363 0.8130464 ]\n",
      "[0.9103223  0.08967771]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.91071403 0.08928595]\n",
      "[0.04209413 0.9579059 ]\n",
      "[0.6519562  0.34804383]\n",
      "[0.22825016 0.7717499 ]\n",
      "[0.9071319  0.09286811]\n",
      "[0.2755769 0.7244231]\n",
      "[0.55184644 0.4481535 ]\n",
      "[0.06658153 0.9334185 ]\n",
      "[0.90793896 0.09206107]\n",
      "[0.10516099 0.894839  ]\n",
      "[0.6326834 0.3673166]\n",
      "[0.36656216 0.6334378 ]\n",
      "[0.46817344 0.53182656]\n",
      "[0.05808995 0.9419101 ]\n",
      "[0.5310178  0.46898228]\n",
      "[0.04435072 0.95564926]\n",
      "[0.9071319  0.09286811]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.8875847  0.11241531]\n",
      "[0.04435072 0.95564926]\n",
      "[0.5928935  0.40710652]\n",
      "[0.11332195 0.88667804]\n",
      "[0.907052   0.09294799]\n",
      "[0.04209413 0.9579059 ]\n",
      "[0.9071319  0.09286811]\n",
      "[0.3285391 0.6714609]\n",
      "[0.8367832  0.16321681]\n",
      "[0.22825016 0.7717499 ]\n",
      "[0.5724947  0.42750528]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.8478281  0.15217194]\n",
      "[0.310303   0.68969697]\n",
      "[0.5724947  0.42750528]\n",
      "[0.17453368 0.8254663 ]\n",
      "[0.7066892  0.29331085]\n",
      "[0.04921436 0.9507857 ]\n",
      "[0.6519562  0.34804383]\n",
      "[0.06658153 0.9334185 ]\n",
      "[0.90793896 0.09206107]\n",
      "[0.06197108 0.93802893]\n",
      "[0.911493 0.088507]\n",
      "[0.3285391 0.6714609]\n",
      "[0.91110426 0.08889569]\n",
      "[0.09752297 0.902477  ]\n",
      "[0.909929   0.09007103]\n",
      "[0.24336565 0.7566343 ]\n",
      "[0.75603586 0.24396418]\n",
      "[0.14117804 0.858822  ]\n",
      "[0.6326834 0.3673166]\n",
      "[0.10516099 0.894839  ]\n",
      "[0.82499784 0.17500217]\n",
      "[0.04921436 0.9507857 ]\n",
      "[0.9043469  0.09565307]\n",
      "[0.06658153 0.9334185 ]\n",
      "[0.5928935  0.40710652]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.5100806  0.48991945]\n",
      "[0.12202995 0.87797004]\n",
      "[0.78564525 0.21435472]\n",
      "[0.3285391 0.6714609]\n",
      "[0.7066892  0.29331085]\n",
      "[0.11332195 0.88667804]\n",
      "[0.89308345 0.10691648]\n",
      "[0.12202995 0.87797004]\n",
      "[0.911493 0.088507]\n",
      "[0.18695363 0.8130464 ]\n",
      "[0.8875847  0.11241531]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.55184644 0.4481535 ]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.90953404 0.09046593]\n",
      "[0.21380827 0.78619176]\n",
      "[0.78564525 0.21435472]\n",
      "[0.3285391 0.6714609]\n",
      "[0.8813092  0.11869076]\n",
      "[0.08371867 0.91628134]\n",
      "[0.90953404 0.09046593]\n",
      "[0.05475008 0.9452499 ]\n",
      "[0.44735056 0.5526495 ]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.79943657 0.20056346]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.82499784 0.17500217]\n",
      "[0.21380827 0.78619176]\n",
      "[0.907052   0.09294799]\n",
      "[0.09752297 0.902477  ]\n",
      "[0.9103969  0.08960312]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.9043469  0.09565306]\n",
      "[0.12202995 0.87797004]\n",
      "[0.81255215 0.18744786]\n",
      "[0.05475008 0.9452499 ]\n",
      "[0.909929   0.09007103]\n",
      "[0.18695363 0.8130464 ]\n",
      "[0.90753627 0.09246378]\n",
      "[0.04435072 0.95564926]\n",
      "[0.90753627 0.09246378]\n",
      "[0.07179504 0.928205  ]\n",
      "[0.9103969  0.08960312]\n",
      "[0.07179504 0.928205  ]\n",
      "[0.78564525 0.21435472]\n",
      "[0.04209413 0.9579059 ]\n",
      "[0.5928935  0.40710652]\n",
      "[0.17453368 0.8254663 ]\n",
      "[0.8745715  0.12542857]\n",
      "[0.16277368 0.8372264 ]\n",
      "[0.81255215 0.18744786]\n",
      "[0.34730724 0.6526928 ]\n",
      "[0.46817344 0.53182656]\n",
      "[0.17453368 0.8254663 ]\n",
      "[0.67074406 0.32925594]\n",
      "[0.05475008 0.9452499 ]\n",
      "[0.9087396  0.09126034]\n",
      "[0.10516099 0.894839  ]\n",
      "[0.91110426 0.08889569]\n",
      "[0.2755769 0.7244231]\n",
      "[0.89774567 0.10225436]\n",
      "[0.04435072 0.95564926]\n",
      "[0.907052 0.092948]\n",
      "[0.38625285 0.6137471 ]\n",
      "[0.55184644 0.4481535 ]\n",
      "[0.4063228 0.5936771]\n",
      "[0.90883845 0.09116157]\n",
      "[0.11332195 0.88667804]\n",
      "[0.82499784 0.17500217]\n",
      "[0.4063228 0.5936771]\n",
      "[0.8367832  0.16321681]\n",
      "[0.14117804 0.858822  ]\n",
      "[0.5724947  0.42750528]\n",
      "[0.0903838  0.90961623]\n",
      "[0.7711772  0.22882277]\n",
      "[0.04209413 0.9579059 ]\n",
      "[0.91110426 0.08889569]\n",
      "[0.24336565 0.7566343 ]\n",
      "[0.9116633  0.08833666]\n",
      "[0.04672239 0.9532775 ]\n",
      "[0.48910788 0.51089215]\n",
      "[0.04209413 0.9579059 ]\n",
      "[0.48910788 0.51089215]\n",
      "[0.07750327 0.92249674]\n",
      "[0.68900144 0.31099856]\n",
      "[0.18695363 0.8130464 ]\n",
      "[0.55184644 0.4481535 ]\n",
      "[0.34730724 0.6526928 ]\n",
      "[0.79943657 0.20056346]\n",
      "[0.04921436 0.9507857 ]\n",
      "[0.8813092  0.11869076]\n",
      "[0.34730724 0.6526928 ]\n",
      "[0.68900144 0.31099856]\n",
      "[0.20004316 0.7999568 ]\n",
      "[0.9015458  0.09845416]\n",
      "[0.13130808 0.8686919 ]\n",
      "[0.78564525 0.21435472]\n",
      "[0.18695363 0.8130464 ]\n",
      "[0.6519562  0.34804383]\n",
      "[0.25914598 0.740854  ]\n",
      "[0.7066892  0.29331085]\n",
      "[0.05183201 0.948168  ]\n",
      "[0.8666112  0.13338882]\n",
      "[0.16277368 0.8372264 ]\n"
     ]
    }
   ],
   "source": [
    "for i in predictions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hvADYQhZcPNj"
   },
   "outputs": [],
   "source": [
    "rounded_predictions = model.predict_classes(x=scaled_test_samples, batch_size=10,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5132,
     "status": "ok",
     "timestamp": 1590893773005,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "dBihQgtCcpNe",
    "outputId": "3f0a08c6-736b-4e93-a46f-135eb1c6a4b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in rounded_predictions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W7a12KbHct2z"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_EptHUhrfDPl"
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_true=test_labels, y_pred=rounded_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sSRSDX2MfUEU"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "    normalize=False,\n",
    "    title='Confusion matrix',\n",
    "    cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHDlEygqfXtR"
   },
   "outputs": [],
   "source": [
    "cm_plot_lables = [\"no_side_effects\", \"had_side_effects\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5079,
     "status": "ok",
     "timestamp": 1590893773008,
     "user": {
      "displayName": "Yuan Zhang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi3Wc5UPsnzyOOU8mc-eL5R_obQ8CcguTHHB_0=s64",
      "userId": "14506763026835673409"
     },
     "user_tz": -480
    },
    "id": "jM995jmGfhr7",
    "outputId": "880e75ed-2ce5-4c6d-c302-ce45fd5f164c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[190  20]\n",
      " [  9 201]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm=cm, classes=cm_plot_lables, title=\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YvvJ2-xSfvjs"
   },
   "outputs": [],
   "source": [
    "model.save(\"medical_trail_predict.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zLuEEKZEigok"
   },
   "outputs": [],
   "source": [
    "from keras import quantized_model\n",
    "quantized_medical_model = quantized_model(model)\n",
    "model.save(\"medical_trail_predict.h5\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "first_train_model.ipynb",
   "provenance": [
    {
     "file_id": "1BXpdEN7Ju4b1q7J5orrsJKQ4lkVs_u3Z",
     "timestamp": 1590811953231
    },
    {
     "file_id": "https://github.com/pytorch/tutorials/blob/gh-pages/_downloads/tensor_tutorial.ipynb",
     "timestamp": 1590155407086
    }
   ]
  },
  "kernelspec": {
   "display_name": "opencv",
   "language": "python",
   "name": "opencv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
